Abstract: In order to check the spread of objectionable video and sound multimedia information in the internet, this paper proposes an objectionable sound recognition method based on shifted delta cepstral features. The proposed method partitions the input sound signal into frames, and then extracts the shifted delta cepstral features on every frame. The method adopts Gaussian mixture model to preliminary classify the frame. The objectionable frames are verified by Support Vector Machine（SVM）. The experimental results demonstrate the proposed method has high accurate recognition rate and low error recognition rate, and can be used to filter the objectionable sounds and videos in the Internet.

Key words: objectionable information filter, objectionable sounds recognition, shifted delta cepstral features, Gaussian Maxture Model（GMM）, Support Vector Machine（SVM）

摘要： 为了应对低俗视频语音等多媒体信息在网络上的大量传播，提出了一种基于移位差分倒谱参数特征的低俗语音识别方法。该方法对输入的语音信号进行分帧，提取移位差分倒谱参数特征，采用了高斯混合模型进行粗分类，对粗分为低俗的语音帧再用支持向量机分类器进行确认。实验结果表明，该方法具有较高的正识别率和较低的误识别率，可用于网络上低俗语音和视频信息的过滤。

关键词: 不良信息过滤, 低俗语音识别, 移位差分倒谱参数, 高斯混合模型, 支持向量机